Pandemic-and Future-Proofing Cities: Pedestrian-oriented Development as an Alternative Model to Transit-based Intensification Centers
Bibliographic record
Abstract
Introduction Many official smart growth-inspired Canadian plans limit sprawl by mixing land uses, transportation modes, jobs, and residents to create compact, transit-oriented, multi-functional intensification centers enriched with amenities and highly designed public spaces (Ontario Government Ministry of Municipal Affairs and Housing, 2019 [2006]; City of Toronto, 2018). However, these intensification strategies, built on new or expanded public transit systems at metropolitan, regional, and local planning scales, face challenges amid the 2020 pandemic (Filion et al, 2016). Recovery from the combined COVID-19-induced loss of commercial activity in intensification centers and confidence in public transit could take years, and combined with an increased reliance on private vehicles, could undo decades of planning efforts at shifting unsustainable land use-transportation dynamics. Concurrently, there is growing attention on sustainable cities with ample public spaces where safe walking and cycling can flourish. Advocates call for reclaiming the streets for people, pedestrians, and cyclists as a resilient strategy for cities and healthy living (Ewing, 2020a). Cities like Milan, Paris, New York, and Seattle are making permanent, temporary space accommodations to pandemic-related pedestrian flows and distancing (Laker, 2020). This chapter is based on the Canadian (and to a large extent North American) urban reality, which is dominated by lowdensity, functionally-specialized, and automobile-oriented land uses. Over the last decades, planning efforts to modify this urban form took the form of high-density intensification centers focused on existing or new public transit rail or BRT (bus rapid transit) systems. Such a strategy faces mounting uncertainty amid pandemic-induced, and possibly long-lasting, transit ridership, brick and mortar retailing, and office work decline. We propose as an alternative, or complementary, intensification approach, a pedestrian-oriented development (POD) model inspired by the ‘15-minute city’ being considered across the world. The chapter refers to transit-oriented developments and other attempts at concentrating density and multifunctionality as intensification centers. Different forms of intensification centers share as an objective the creation of spaces that contrast with the North American low-density car-dependent norm. One version of intensification centers discussed here is primarily transit-oriented while the other is more focused on a pedestrian-hospitable environment. The impacts of COVID-19 on transit-based intensification centers COVID-19 impacts multiple facets of intensification centers organized around transit and amplifies issues present prior to the pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".